tgls

Intel Core i7-1185G7 testing with a Dell 0DXP1F (3.7.0 BIOS) and Intel Xe TGL GT2 15GB on Ubuntu 22.04 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2311135-PTS-TGLS547809
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AV1 2 Tests
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Creator Workloads 11 Tests
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HPC - High Performance Computing 7 Tests
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November 12 2023
  8 Hours, 30 Minutes
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November 12 2023
  21 Hours, 56 Minutes
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November 13 2023
  21 Hours, 46 Minutes
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  17 Hours, 24 Minutes

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tgls Suite 1.0.0 System Test suite extracted from tgls. pts/build-gcc-1.4.0 Time To Compile pts/build-gem5-1.1.0 Time To Compile pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/ncnn-1.5.0 -1 Target: CPU - Model: FastestDet pts/ncnn-1.5.0 -1 Target: CPU - Model: vision_transformer pts/ncnn-1.5.0 -1 Target: CPU - Model: regnety_400m pts/ncnn-1.5.0 -1 Target: CPU - Model: squeezenet_ssd pts/ncnn-1.5.0 -1 Target: CPU - Model: yolov4-tiny pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet50 pts/ncnn-1.5.0 -1 Target: CPU - Model: alexnet pts/ncnn-1.5.0 -1 Target: CPU - Model: resnet18 pts/ncnn-1.5.0 -1 Target: CPU - Model: vgg16 pts/ncnn-1.5.0 -1 Target: CPU - Model: googlenet pts/ncnn-1.5.0 -1 Target: CPU - Model: blazeface pts/ncnn-1.5.0 -1 Target: CPU - Model: efficientnet-b0 pts/ncnn-1.5.0 -1 Target: CPU - Model: mnasnet pts/ncnn-1.5.0 -1 Target: CPU - Model: shufflenet-v2 pts/ncnn-1.5.0 -1 Target: CPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.5.0 -1 Target: CPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.5.0 -1 Target: CPU - Model: mobilenet pts/ncnn-1.5.0 Target: Vulkan GPU - Model: FastestDet pts/ncnn-1.5.0 Target: Vulkan GPU - Model: vision_transformer pts/ncnn-1.5.0 Target: Vulkan GPU - Model: regnety_400m pts/ncnn-1.5.0 Target: Vulkan GPU - Model: squeezenet_ssd pts/ncnn-1.5.0 Target: Vulkan GPU - Model: yolov4-tiny pts/ncnn-1.5.0 Target: Vulkan GPU - Model: resnet50 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: alexnet pts/ncnn-1.5.0 Target: Vulkan GPU - Model: resnet18 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: vgg16 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: googlenet pts/ncnn-1.5.0 Target: Vulkan GPU - Model: blazeface pts/ncnn-1.5.0 Target: Vulkan GPU - Model: efficientnet-b0 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: mnasnet pts/ncnn-1.5.0 Target: Vulkan GPU - Model: shufflenet-v2 pts/ncnn-1.5.0 Target: Vulkan GPU-v3-v3 - Model: mobilenet-v3 pts/ncnn-1.5.0 Target: Vulkan GPU-v2-v2 - Model: mobilenet-v2 pts/ncnn-1.5.0 Target: Vulkan GPU - Model: mobilenet pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 3840 2160 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/openvkl-2.0.0 vklBenchmarkCPU --benchmark_filter=ispc Benchmark: vklBenchmarkCPU ISPC pts/brl-cad-1.5.0 VGR Performance Metric pts/qmcpack-1.7.0 tests/molecules/Li2_STO_ae Li2.STO.long.in.xml Input: Li2_STO_ae pts/openradioss-1.1.1 BIRD_WINDSHIELD_v1_0000.rad BIRD_WINDSHIELD_v1_0001.rad Model: Bird Strike on Windshield pts/openvkl-2.0.0 vklBenchmarkCPU --benchmark_filter=scalar Benchmark: vklBenchmarkCPU Scalar pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/openradioss-1.1.1 RUBBER_SEAL_IMPDISP_GEOM_0000.rad RUBBER_SEAL_IMPDISP_GEOM_0001.rad Model: Rubber O-Ring Seal Installation pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 3840 2160 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/oidn-2.1.0 -r RTLightmap.hdr.4096x4096 -d cpu Run: RTLightmap.hdr.4096x4096 - Device: CPU-Only pts/avifenc-1.4.0 -s 0 Encoder Speed: 0 pts/vvenc-1.9.1 -i Bosphorus_3840x2160.y4m --preset fast Video Input: Bosphorus 4K - Video Preset: Fast pts/ffmpeg-6.1.0 --encoder=libx265 vod Encoder: libx265 - Scenario: Video On Demand pts/ffmpeg-6.1.0 --encoder=libx265 platform Encoder: libx265 - Scenario: Platform pts/ffmpeg-6.1.0 --encoder=libx265 upload Encoder: libx265 - Scenario: Upload pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/ffmpeg-6.1.0 --encoder=libx264 upload Encoder: libx264 - Scenario: Upload pts/openradioss-1.1.1 Cell_Phone_Drop_0000.rad Cell_Phone_Drop_0001.rad Model: Cell Phone Drop Test pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 1920 1080 --spp 32 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 32 - Renderer: Path Tracer - Acceleration: CPU pts/qmcpack-1.7.0 tests/molecules/O_ae_pyscf_UHF vmc_long_noj.in.xml Input: O_ae_pyscf_UHF pts/cloverleaf-1.2.0 clover_bm64_short Input: clover_bm64_short pts/oidn-2.1.0 -r RT.hdr_alb_nrm.3840x2160 -d cpu Run: RT.hdr_alb_nrm.3840x2160 - Device: CPU-Only pts/oidn-2.1.0 -r RT.ldr_alb_nrm.3840x2160 -d cpu Run: RT.ldr_alb_nrm.3840x2160 - Device: CPU-Only pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 1200 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 1200 pts/embree-1.6.0 pathtracer -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon Obj pts/embree-1.6.0 pathtracer -c crown/crown.ecs Binary: Pathtracer - Model: Crown pts/ffmpeg-6.1.0 --encoder=libx264 vod Encoder: libx264 - Scenario: Video On Demand pts/ffmpeg-6.1.0 --encoder=libx264 platform Encoder: libx264 - Scenario: Platform pts/avifenc-1.4.0 -s 2 Encoder Speed: 2 pts/vvenc-1.9.1 -i Bosphorus_3840x2160.y4m --preset faster Video Input: Bosphorus 4K - Video Preset: Faster pts/embree-1.6.0 pathtracer_ispc -c asian_dragon_obj/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon Obj pts/embree-1.6.0 pathtracer_ispc -c crown/crown.ecs Binary: Pathtracer ISPC - Model: Crown pts/embree-1.6.0 pathtracer -c asian_dragon/asian_dragon.ecs Binary: Pathtracer - Model: Asian Dragon pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/svt-av1-2.10.0 --preset 4 -n 160 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 4 - Input: Bosphorus 4K pts/cassandra-1.2.0 WRITE Test: Writes pts/build-ffmpeg-6.1.0 Time To Compile pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 1920 1080 --spp 16 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 16 - Renderer: Path Tracer - Acceleration: CPU pts/embree-1.6.0 pathtracer_ispc -c asian_dragon/asian_dragon.ecs Binary: Pathtracer ISPC - Model: Asian Dragon pts/qmcpack-1.7.0 tests/molecules/FeCO6_b3lyp_gms vmc_long_noj.in.xml Input: FeCO6_b3lyp_gms pts/cloverleaf-1.2.0 clover_bm Input: clover_bm pts/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/base-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering - Scenario: Asynchronous Multi-Stream pts/vvenc-1.9.1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset fast Video Input: Bosphorus 1080p - Video Preset: Fast pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_training --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Training - Data Type: f32 - Engine: CPU pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 3 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 1920 1080 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 1080p - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: f32 - Engine: CPU pts/ffmpeg-6.1.0 --encoder=libx265 live Encoder: libx265 - Scenario: Live pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=bf16bf16bf16 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --rnn --batch=inputs/rnn/perf_rnn_inference_lb --cfg=u8s8f32 --engine=cpu Harness: Recurrent Neural Network Inference - Data Type: u8s8f32 - Engine: CPU pts/qmcpack-1.7.0 tests/molecules/LiH_ae_MSD vmc_long_opt_CI.in.xml Input: LiH_ae_MSD pts/ospray-studio-1.2.0 --cameras 1 1 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 1 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/qmcpack-1.7.0 tests/molecules/H4_ae optm-linear-linemin.xml Input: H4_ae pts/openvino-1.4.0 -m models/intel/face-detection-0206/FP16/face-detection-0206.xml -d CPU Model: Face Detection FP16 - Device: CPU pts/dacapobench-1.1.0 cassandra Java Test: Apache Cassandra pts/openvino-1.4.0 -m models/intel/face-detection-0206/FP16-INT8/face-detection-0206.xml -d CPU Model: Face Detection FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/machine-translation-nar-en-de-0002/FP16/machine-translation-nar-en-de-0002.xml -d CPU Model: Machine Translation EN To DE FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/person-detection-0303/FP32/person-detection-0303.xml -d CPU Model: Person Detection FP32 - Device: CPU pts/openvino-1.4.0 -m models/intel/person-detection-0303/FP16/person-detection-0303.xml -d CPU Model: Person Detection FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/road-segmentation-adas-0001/FP16-INT8/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/person-vehicle-bike-detection-2004/FP16/person-vehicle-bike-detection-2004.xml -d CPU Model: Person Vehicle Bike Detection FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/road-segmentation-adas-0001/FP16/road-segmentation-adas-0001.xml -d CPU Model: Road Segmentation ADAS FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/handwritten-english-recognition-0001/FP16-INT8/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/handwritten-english-recognition-0001/FP16/handwritten-english-recognition-0001.xml -d CPU Model: Handwritten English Recognition FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/vehicle-detection-0202/FP16-INT8/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/face-detection-retail-0005/FP16-INT8/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/vehicle-detection-0202/FP16/vehicle-detection-0202.xml -d CPU Model: Vehicle Detection FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/weld-porosity-detection-0001/FP16/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/weld-porosity-detection-0001/FP16-INT8/weld-porosity-detection-0001.xml -d CPU Model: Weld Porosity Detection FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/face-detection-retail-0005/FP16/face-detection-retail-0005.xml -d CPU Model: Face Detection Retail FP16 - Device: CPU pts/openvino-1.4.0 -m models/intel/age-gender-recognition-retail-0013/FP16-INT8/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16-INT8 - Device: CPU pts/openvino-1.4.0 -m models/intel/age-gender-recognition-retail-0013/FP16/age-gender-recognition-retail-0013.xml -d CPU Model: Age Gender Recognition Retail 0013 FP16 - Device: CPU pts/deepsparse-1.5.2 zoo:nlp/question_answering/obert-large/pytorch/huggingface/squad/pruned97_quant-none --input_shapes='[1,128]' --scenario async Model: BERT-Large, NLP Question Answering, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/quantlib-1.2.0 --mp Configuration: Multi-Threaded pts/deepsparse-1.5.2 zoo:nlp/sentiment_analysis/oberta-base/pytorch/huggingface/sst2/pruned90_quant-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/token_classification/bert-base/pytorch/huggingface/conll2003/base-none --scenario async Model: NLP Token Classification, BERT base uncased conll2003 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none --scenario async Model: CV Segmentation, 90% Pruned YOLACT Pruned - Scenario: Asynchronous Multi-Stream pts/ffmpeg-6.1.0 --encoder=libx264 live Encoder: libx264 - Scenario: Live pts/deepsparse-1.5.2 zoo:nlp/text_classification/bert-base/pytorch/huggingface/sst2/base-none --input_shapes='[1,128]' --scenario async Model: NLP Text Classification, BERT base uncased SST2 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:nlp/document_classification/obert-base/pytorch/huggingface/imdb/base-none --scenario async Model: NLP Document Classification, oBERT base uncased on IMDB - Scenario: Asynchronous Multi-Stream pts/dacapobench-1.1.0 eclipse Java Test: Eclipse pts/dacapobench-1.1.0 tomcat Java Test: Apache Tomcat pts/vvenc-1.9.1 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.y4m --preset faster Video Input: Bosphorus 1080p - Video Preset: Faster pts/deepsparse-1.5.2 zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/12layer_pruned90-none --scenario async Model: NLP Question Answering, BERT base uncased SQuaD 12layer Pruned90 - Scenario: Asynchronous Multi-Stream pts/ospray-studio-1.2.0 --cameras 3 3 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 3 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/deepsparse-1.5.2 zoo:nlp/sentiment_analysis/bert-base/pytorch/huggingface/sst2/12layer_pruned90-none --scenario async Model: NLP Sentiment Analysis, 80% Pruned Quantized BERT Base Uncased - Scenario: Asynchronous Multi-Stream pts/dacapobench-1.1.0 h2 Java Test: H2 Database Engine pts/ospray-studio-1.2.0 --cameras 2 2 --resolution 3840 2160 --spp 1 --renderer pathtracer Camera: 2 - Resolution: 4K - Samples Per Pixel: 1 - Renderer: Path Tracer - Acceleration: CPU pts/svt-av1-2.10.0 --preset 8 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 8 - Input: Bosphorus 4K pts/deepsparse-1.5.2 zoo:nlp/text_classification/distilbert-none/pytorch/huggingface/mnli/base-none --scenario async Model: NLP Text Classification, DistilBERT mnli - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: ResNet-50, Baseline - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned85-none --scenario async Model: CV Detection, YOLOv5s COCO, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none --scenario async Model: CV Classification, ResNet-50 ImageNet - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_uniform_quant-none --scenario async Model: ResNet-50, Sparse INT8 - Scenario: Asynchronous Multi-Stream pts/deepsparse-1.5.2 zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none --scenario async Model: CV Detection, YOLOv5s COCO - Scenario: Asynchronous Multi-Stream pts/svt-av1-2.10.0 --preset 4 -n 160 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 4 - Input: Bosphorus 1080p pts/stress-ng-1.11.0 --io-uring -1 --no-rand-seed Test: IO_uring pts/dacapobench-1.1.0 luindex Java Test: Apache Lucene Search Index pts/dacapobench-1.1.0 tradebeans Java Test: Tradebeans pts/qmcpack-1.7.0 build/examples/molecules/H2O/example_H2O-1-1 simple-H2O.xml Input: simple-H2O pts/stress-ng-1.11.0 --mmap -1 --no-rand-seed Test: MMAP pts/stress-ng-1.11.0 --clone -1 --no-rand-seed Test: Cloning pts/stress-ng-1.11.0 --malloc -1 --no-rand-seed Test: Malloc pts/stress-ng-1.11.0 --memfd -1 --no-rand-seed Test: MEMFD pts/cpuminer-opt-1.7.0 -a allium Algorithm: Garlicoin pts/stress-ng-1.11.0 --rdrand -1 --no-rand-seed Test: x86_64 RdRand pts/stress-ng-1.11.0 --zlib -1 --no-rand-seed Test: Zlib pts/cpuminer-opt-1.7.0 -a sha256q Algorithm: Quad SHA-256, Pyrite pts/stress-ng-1.11.0 --numa -1 --no-rand-seed Test: NUMA pts/stress-ng-1.11.0 --atomic -1 --no-rand-seed Test: Atomic pts/stress-ng-1.11.0 --fp -1 --no-rand-seed Test: Floating Point pts/stress-ng-1.11.0 --funccall -1 --no-rand-seed Test: Function Call pts/stress-ng-1.11.0 --pthread -1 --no-rand-seed Test: Pthread pts/stress-ng-1.11.0 --hash -1 --no-rand-seed Test: Hash pts/stress-ng-1.11.0 --futex -1 --no-rand-seed Test: Futex pts/stress-ng-1.11.0 --sendfile -1 --no-rand-seed Test: SENDFILE pts/stress-ng-1.11.0 --vecfp -1 --no-rand-seed Test: Vector Floating Point pts/stress-ng-1.11.0 --fma -1 --no-rand-seed Test: Fused Multiply-Add pts/stress-ng-1.11.0 --memcpy -1 --no-rand-seed Test: Memory Copying pts/stress-ng-1.11.0 --matrix-3d -1 --no-rand-seed Test: Matrix 3D Math pts/stress-ng-1.11.0 --schedmix -1 Test: Mixed Scheduler pts/stress-ng-1.11.0 --tree -1 --tree-method avl --no-rand-seed Test: AVL Tree pts/stress-ng-1.11.0 --fork -1 --no-rand-seed Test: Forking pts/stress-ng-1.11.0 --crypt -1 --no-rand-seed Test: Crypto pts/stress-ng-1.11.0 --mutex -1 --no-rand-seed Test: Mutex pts/stress-ng-1.11.0 --poll -1 --no-rand-seed Test: Poll pts/stress-ng-1.11.0 --pipe -1 --no-rand-seed Test: Pipe pts/stress-ng-1.11.0 --matrix -1 --no-rand-seed Test: Matrix Math pts/stress-ng-1.11.0 --cache -1 --no-rand-seed Test: CPU Cache pts/stress-ng-1.11.0 --msg -1 --no-rand-seed Test: System V Message Passing pts/stress-ng-1.11.0 --qsort -1 --no-rand-seed Test: Glibc Qsort Data Sorting pts/stress-ng-1.11.0 --str -1 --no-rand-seed Test: Glibc C String Functions pts/stress-ng-1.11.0 --switch -1 --no-rand-seed Test: Context Switching pts/stress-ng-1.11.0 --vecwide -1 --no-rand-seed Test: Wide Vector Math pts/stress-ng-1.11.0 --sock -1 --no-rand-seed --sock-zerocopy Test: Socket Activity pts/stress-ng-1.11.0 --vecshuf -1 --no-rand-seed Test: Vector Shuffle pts/stress-ng-1.11.0 --vnni -1 Test: AVX-512 VNNI pts/stress-ng-1.11.0 --vecmath -1 --no-rand-seed Test: Vector Math pts/stress-ng-1.11.0 --sem -1 --no-rand-seed Test: Semaphores pts/stress-ng-1.11.0 --cpu -1 --cpu-method all --no-rand-seed Test: CPU Stress pts/cpuminer-opt-1.7.0 -a sha256t Algorithm: Triple SHA-256, Onecoin pts/cpuminer-opt-1.7.0 -a blake2s Algorithm: Blake-2 S pts/cpuminer-opt-1.7.0 -a lbry Algorithm: LBC, LBRY Credits pts/cpuminer-opt-1.7.0 -a skein Algorithm: Skeincoin pts/cpuminer-opt-1.7.0 -a deep Algorithm: Deepcoin pts/cpuminer-opt-1.7.0 -a myr-gr Algorithm: Myriad-Groestl pts/cpuminer-opt-1.7.0 -a scrypt Algorithm: scrypt pts/cpuminer-opt-1.7.0 -a m7m Algorithm: Magi pts/cpuminer-opt-1.7.0 -a minotaur Algorithm: Ringcoin pts/dacapobench-1.1.0 spring Java Test: Spring Boot pts/quantlib-1.2.0 Configuration: Single-Threaded pts/dacapobench-1.1.0 lusearch Java Test: Apache Lucene Search Engine pts/avifenc-1.4.0 -s 6 -l Encoder Speed: 6, Lossless pts/dacapobench-1.1.0 graphchi Java Test: GraphChi pts/dacapobench-1.1.0 jme Java Test: jMonkeyEngine pts/dacapobench-1.1.0 tradesoap Java Test: Tradesoap pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_1d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_1d - Data Type: f32 - Engine: CPU pts/dacapobench-1.1.0 biojava Java Test: BioJava Biological Data Framework pts/dacapobench-1.1.0 kafka Java Test: Apache Kafka pts/dacapobench-1.1.0 jython Java Test: Jython pts/svt-av1-2.10.0 --preset 12 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 12 - Input: Bosphorus 4K pts/dacapobench-1.1.0 h2o Java Test: H2O In-Memory Platform For Machine Learning pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_1d --cfg=f32 --engine=cpu Harness: IP Shapes 1D - Data Type: f32 - Engine: CPU pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_1d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 1D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_1d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 1D - Data Type: u8s8f32 - Engine: CPU pts/svt-av1-2.10.0 --preset 8 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 8 - Input: Bosphorus 1080p pts/avifenc-1.4.0 -s 6 Encoder Speed: 6 pts/svt-av1-2.10.0 --preset 13 -i Bosphorus_3840x2160.y4m -w 3840 -h 2160 Encoder Mode: Preset 13 - Input: Bosphorus 4K pts/dacapobench-1.1.0 pmd Java Test: PMD Source Code Analyzer pts/dacapobench-1.1.0 zxing Java Test: Zxing 1D/2D Barcode Image Processing pts/easywave-1.0.0 -grid examples/e2Asean.grd -source examples/BengkuluSept2007.flt -time 240 Input: e2Asean Grid + BengkuluSept2007 Source - Time: 240 pts/avifenc-1.4.0 -s 10 -l Encoder Speed: 10, Lossless pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: IP Shapes 3D - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_3d --cfg=f32 --engine=cpu Harness: IP Shapes 3D - Data Type: f32 - Engine: CPU pts/onednn-3.3.0 --ip --batch=inputs/ip/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: IP Shapes 3D - Data Type: u8s8f32 - Engine: CPU pts/blosc-1.3.0 blosclz noshuffle 268435456 Test: blosclz noshuffle - Buffer Size: 256MB pts/blosc-1.3.0 blosclz bitshuffle 268435456 Test: blosclz bitshuffle - Buffer Size: 256MB pts/blosc-1.3.0 blosclz shuffle 268435456 Test: blosclz shuffle - Buffer Size: 256MB pts/dacapobench-1.1.0 avrora Java Test: Avrora AVR Simulation Framework pts/blosc-1.3.0 blosclz noshuffle 134217728 Test: blosclz noshuffle - Buffer Size: 128MB pts/dacapobench-1.1.0 xalan Java Test: Apache Xalan XSLT pts/blosc-1.3.0 blosclz bitshuffle 134217728 Test: blosclz bitshuffle - Buffer Size: 128MB pts/dacapobench-1.1.0 batik Java Test: Batik SVG Toolkit pts/blosc-1.3.0 blosclz shuffle 134217728 Test: blosclz shuffle - Buffer Size: 128MB pts/onednn-3.3.0 --conv --batch=inputs/conv/shapes_auto --cfg=bf16bf16bf16 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --conv --batch=inputs/conv/shapes_auto --cfg=u8s8f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: u8s8f32 - Engine: CPU pts/onednn-3.3.0 --conv --batch=inputs/conv/shapes_auto --cfg=f32 --engine=cpu Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU pts/blosc-1.3.0 blosclz noshuffle 67108864 Test: blosclz noshuffle - Buffer Size: 64MB pts/blosc-1.3.0 blosclz bitshuffle 67108864 Test: blosclz bitshuffle - Buffer Size: 64MB pts/blosc-1.3.0 blosclz shuffle 67108864 Test: blosclz shuffle - Buffer Size: 64MB pts/blosc-1.3.0 blosclz noshuffle 33554432 Test: blosclz noshuffle - Buffer Size: 32MB pts/blosc-1.3.0 blosclz bitshuffle 33554432 Test: blosclz bitshuffle - Buffer Size: 32MB pts/blosc-1.3.0 blosclz shuffle 33554432 Test: blosclz shuffle - Buffer Size: 32MB pts/blosc-1.3.0 blosclz noshuffle 16777216 Test: blosclz noshuffle - Buffer Size: 16MB pts/blosc-1.3.0 blosclz shuffle 16777216 Test: blosclz shuffle - Buffer Size: 16MB pts/blosc-1.3.0 blosclz bitshuffle 16777216 Test: blosclz bitshuffle - Buffer Size: 16MB pts/blosc-1.3.0 blosclz noshuffle 8388608 Test: blosclz noshuffle - Buffer Size: 8MB pts/blosc-1.3.0 blosclz bitshuffle 8388608 Test: blosclz bitshuffle - Buffer Size: 8MB pts/blosc-1.3.0 blosclz shuffle 8388608 Test: blosclz shuffle - Buffer Size: 8MB pts/dacapobench-1.1.0 fop Java Test: FOP Print Formatter pts/svt-av1-2.10.0 --preset 12 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 12 - Input: Bosphorus 1080p pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=bf16bf16bf16 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: bf16bf16bf16 - Engine: CPU pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: f32 - Engine: CPU pts/onednn-3.3.0 --deconv --batch=inputs/deconv/shapes_3d --cfg=u8s8f32 --engine=cpu Harness: Deconvolution Batch shapes_3d - Data Type: u8s8f32 - Engine: CPU pts/svt-av1-2.10.0 --preset 13 -i Bosphorus_1920x1080_120fps_420_8bit_YUV.yuv -w 1920 -h 1080 Encoder Mode: Preset 13 - Input: Bosphorus 1080p pts/rabbitmq-1.1.1 -x 2 -y 4 -u "throughput-test-2" -a --id "test 2" -s 8000 Scenario: Simple 2 Publishers + 4 Consumers pts/rabbitmq-1.1.1 --queue-pattern 'perf-test-%d' --queue-pattern-from 1 --queue-pattern-to 200 --producers 400 --consumers 400 -s 8000 Scenario: 200 Queues, 400 Producers, 400 Consumers pts/rabbitmq-1.1.1 --queue-pattern 'perf-test-%d' --queue-pattern-from 1 --queue-pattern-to 120 --producers 400 --consumers 400 -s 8000 Scenario: 120 Queues, 400 Producers, 400 Consumers pts/rabbitmq-1.1.1 --queue-pattern 'perf-test-%d' --queue-pattern-from 1 --queue-pattern-to 10 --producers 100 --consumers 100 -s 8000 Scenario: 10 Queues, 100 Producers, 100 Consumers pts/rabbitmq-1.1.1 --queue-pattern 'perf-test-%d' --queue-pattern-from 1 --queue-pattern-to 60 --producers 100 --consumers 100 -s 8000 Scenario: 60 Queues, 100 Producers, 100 Consumers